Pub Date : 2023-07-27DOI: 10.1175/jamc-d-22-0127.1
Daniel P. Greenway, T. Haack, E. Hackett
This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island Field Experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground-truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared to this “ground-truth” refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the “ground-truth” parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.
{"title":"Fusing Numerical Weather Prediction Ensembles with Refractivity Inversions During Surface Ducting Conditions","authors":"Daniel P. Greenway, T. Haack, E. Hackett","doi":"10.1175/jamc-d-22-0127.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0127.1","url":null,"abstract":"\u0000This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island Field Experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground-truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared to this “ground-truth” refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the “ground-truth” parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44258792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1175/jamc-d-23-0021.1
Vibha Selvaratnam, D. J. Thomson, H. Webster
The UK Met Office’s atmospheric dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment) is validated against controlled tracer release experiments, considering the impact of the driving meteorology and choices in the parametrization of unresolved motions. CAPTEX (Cross-Appalachian Tracer Experiment) and ANATEX (Across North America Tracer Experiment) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America and Canada in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from ECMWF (European Centre for Medium-Range Weather Forecasts) and data from the Advanced Research version of the WRF (Weather Research and Forecasting) Model. NAME predictions of air concentrations are assessed against the experimental measurements using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parametrization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory), STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorology.
{"title":"Validation of the atmospheric dispersion model NAME against long-range tracer release experiments","authors":"Vibha Selvaratnam, D. J. Thomson, H. Webster","doi":"10.1175/jamc-d-23-0021.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0021.1","url":null,"abstract":"\u0000The UK Met Office’s atmospheric dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment) is validated against controlled tracer release experiments, considering the impact of the driving meteorology and choices in the parametrization of unresolved motions. CAPTEX (Cross-Appalachian Tracer Experiment) and ANATEX (Across North America Tracer Experiment) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America and Canada in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from ECMWF (European Centre for Medium-Range Weather Forecasts) and data from the Advanced Research version of the WRF (Weather Research and Forecasting) Model. NAME predictions of air concentrations are assessed against the experimental measurements using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parametrization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory), STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorology.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49358023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific Index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures decadal variability of annual mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific Decadal Oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extra-tropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability.
{"title":"Seasonality in the Amplitude of Decadal Variability","authors":"Fei Zheng, Jianping Li, Hao Wang, Yuxun Li, Xiaoning Liu, Rui Wang","doi":"10.1175/jamc-d-23-0038.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0038.1","url":null,"abstract":"\u0000As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific Index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures decadal variability of annual mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific Decadal Oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extra-tropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.1175/jamc-d-22-0061.1
Xinyue Zhan, Lei Chen
An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Reanalysis I data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70 year study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°–70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere.
{"title":"Climatology and changes in extratropical cyclone activity in the Southern Hemisphere during austral winters from 1948 to 2017","authors":"Xinyue Zhan, Lei Chen","doi":"10.1175/jamc-d-22-0061.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0061.1","url":null,"abstract":"\u0000An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Reanalysis I data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70 year study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°–70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45457049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1175/jamc-d-23-0057.1
V. Nourani, Kasra Khodkar, A. H. Baghanam, S. Kantoush, I. Demir
This study investigated the uncertainty involved in statistically downscaling of hydroclimatic time series obtained by Artificial Neural Networks (ANNs). The Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Model (GCM) CanESM5 was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANN, feed-forward and long short-term memory (LSTM) were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from Tabriz and Rasht stations were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel-driven development scenario (SSP5-8.5). Projections were compared with the Can-RCM4 projections via same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the FFNN-based predictions with an average of 55% higher Nash-Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience warmer climate by an increase in average temperature by 2 °C and 5 °C for near and far futures, respectively, and drier climate by a 20% decrease in precipitation until 2100. Future projections for the Rasht station however suggested a more uniform climate with less seasonal variability. Average precipitation will increase up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1 °C until near future and then a constant average temperature until far future.
{"title":"Uncertainty quantification of deep learning based statistical downscaling of climatic parameters","authors":"V. Nourani, Kasra Khodkar, A. H. Baghanam, S. Kantoush, I. Demir","doi":"10.1175/jamc-d-23-0057.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0057.1","url":null,"abstract":"\u0000This study investigated the uncertainty involved in statistically downscaling of hydroclimatic time series obtained by Artificial Neural Networks (ANNs). The Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Model (GCM) CanESM5 was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANN, feed-forward and long short-term memory (LSTM) were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from Tabriz and Rasht stations were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel-driven development scenario (SSP5-8.5). Projections were compared with the Can-RCM4 projections via same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the FFNN-based predictions with an average of 55% higher Nash-Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience warmer climate by an increase in average temperature by 2 °C and 5 °C for near and far futures, respectively, and drier climate by a 20% decrease in precipitation until 2100. Future projections for the Rasht station however suggested a more uniform climate with less seasonal variability. Average precipitation will increase up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1 °C until near future and then a constant average temperature until far future.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46575152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1175/jamc-d-22-0175.1
Do-Yun Kim, Hee-Jae Kim, Yong-Sang Choi
Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, image segmentation identifies TCC objects with different cloud properties. Then, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Finally, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from non-precipitating TCCs using Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) precipitation data. Results show that SOM clusters with a high PP (> 70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.
{"title":"Unsupervised Clustering of Geostationary Satellite Cloud Properties for Estimating Precipitation Probabilities of Tropical Convective Clouds","authors":"Do-Yun Kim, Hee-Jae Kim, Yong-Sang Choi","doi":"10.1175/jamc-d-22-0175.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0175.1","url":null,"abstract":"\u0000Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, image segmentation identifies TCC objects with different cloud properties. Then, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Finally, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from non-precipitating TCCs using Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) precipitation data. Results show that SOM clusters with a high PP (> 70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45919251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-10DOI: 10.1175/jamc-d-22-0122.1
G. Katata, R. Connolly, P. O'Neill
In order to reduce the amount of non-climatic biases of air temperature in each weather station’s record by comparing it to neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new non-climatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases”. This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ~60% of the long-term warming). The United States Historical Climatology Network dataset (USHCN) contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ~20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s low estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been biased low due to urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.
{"title":"Evidence of urban blending in homogenized temperature records in Japan and in the United States: implications for the reliability of global land surface air temperature data","authors":"G. Katata, R. Connolly, P. O'Neill","doi":"10.1175/jamc-d-22-0122.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0122.1","url":null,"abstract":"\u0000In order to reduce the amount of non-climatic biases of air temperature in each weather station’s record by comparing it to neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new non-climatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases”. This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ~60% of the long-term warming). The United States Historical Climatology Network dataset (USHCN) contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ~20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s low estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been biased low due to urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45976762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami–Dade County, Florida. Temperature and humidity measurements were collected at sites throughout the county between 2018 and 2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F (3.3°C) higher and maximum heat index values are 11°F (6.1°C) higher at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami–Dade County. We highlight the need for data that are closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.
{"title":"Hyperlocal Observations Reveal Persistent Extreme Urban Heat in Southeast Florida","authors":"Amy Clement, Tiffany Troxler, Oaklin Keefe, Marybeth Arcodia, Mayra Cruz, Alyssa Hernandez, Diana Moanga, Zelalem Adefris, Natalia Brown, Susan Jacobson","doi":"10.1175/jamc-d-22-0165.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0165.1","url":null,"abstract":"Abstract Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami–Dade County, Florida. Temperature and humidity measurements were collected at sites throughout the county between 2018 and 2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F (3.3°C) higher and maximum heat index values are 11°F (6.1°C) higher at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami–Dade County. We highlight the need for data that are closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1175/jamc-d-22-0055.1
Oscar Guzman, Haiyan Jiang
Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs.
{"title":"Climatology of Tropical Cyclone Rainfall Magnitude at Different Landfalling Stages: An Emphasis on After-Landfall Rain","authors":"Oscar Guzman, Haiyan Jiang","doi":"10.1175/jamc-d-22-0055.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0055.1","url":null,"abstract":"\u0000Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain.\u0000\u0000\u0000This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43084763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1175/jamc-d-22-0089.1
Eric Goldenstern, C. Kummerow
Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 (GOES-16) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region’s validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale.
{"title":"Predicting Region-Dependent Biases in a GOES-16 Machine Learning Precipitation Retrieval","authors":"Eric Goldenstern, C. Kummerow","doi":"10.1175/jamc-d-22-0089.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0089.1","url":null,"abstract":"\u0000Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 (GOES-16) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region’s validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44423746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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